STREAK Publication:

Here, we introduce the STREAK (gene Set Testing-based Receptor abundance Estimation using Adjusted distances and cKmeans thresholding) that leverages associations learned from joint scRNA-seq/CITE-seq training data and a thresholded gene set scoring mechanism to estimate receptor abundance for scRNA-seq target data. We evaluate STREAK relative to both unsupervised and supervised receptor abundance estimation techniques using two evaluation approaches on three joint scRNA-seq/CITE-seq datasets that represent two human tissue types. We conclude that STREAK outperforms other abundance estimation strategies and provides a more biologically interpretable and transparent statistical model.



SPECK Publication:

The accurate estimation of cell surface receptor abundance for single cell transcriptomics data is important for the tasks of cell type and phenotype categorization and cell-cell interaction quantification. Here, we introduce an unsupervised receptor abundance estimation technique named SPECK (Surface Protein abundance Estimation using CKmeans-based clustered thresholding) to address the challenges associated with accurate abundance estimation. In this paper, we conclude that SPECK results in improved concordance with Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) data relative to comparative unsupervised abundance estimation techniques using only single-cell RNA-sequencing (scRNA-seq) data.



SCAPE Publication:

Accurate knowledge of cytokine activity levels can be leveraged to provide tailored treatment recommendations based on individual patients’ transcriptomics data. Here, we describe a novel method named Single cell transcriptomics-level Cytokine Activity Prediction and Estimation (SCAPE) that can predict cell-level cytokine activity from scRNA-seq data. SCAPE generates activity estimates using cytokine-specific gene sets constructed using information from the CytoSig and Reactome databases and scored with a modified version of the Variance-adjusted Mahalanobis (VAM) method adjusted for negative weights. Our model has the potential to be incorporated in clinical settings as a way to estimate cytokine signaling for different cell populations within an impacted tissue sample.